package com.gy.hadoop.mr.serialize;

/*
1	13736230513	192.196.100.1	www.atguigu.com	2481	24681	200
2	13846544121	192.196.100.2			264	0	200
3 	13956435636	192.196.100.3			132	1512	200
4 	13966251146	192.168.100.1			240	0	404
5 	18271575951	192.168.100.2	www.atguigu.com	1527	2106	200
6 	84188413	192.168.100.3	www.atguigu.com	4116	1432	200
7 	13590439668	192.168.100.4			1116	954	200
8 	15910133277	192.168.100.5	www.hao123.com	3156	2936	200
9 	13729199489	192.168.100.6			240	0	200
10 	13630577991	192.168.100.7	www.shouhu.com	6960	690	200
11 	15043685818	192.168.100.8	www.baidu.com	3659	3538	200
12 	15959002129	192.168.100.9	www.atguigu.com	1938	180	500
13 	13560439638	192.168.100.10			918	4938	200
14 	13470253144	192.168.100.11			180	180	200
15 	13682846555	192.168.100.12	www.qq.com	1938	2910	200
16 	13992314666	192.168.100.13	www.gaga.com	3008	3720	200
17 	13509468723	192.168.100.14	www.qinghua.com	7335	110349	404
18 	18390173782	192.168.100.15	www.sogou.com	9531	2412	200
19 	13975057813	192.168.100.16	www.baidu.com	11058	48243	200
20 	13768778790	192.168.100.17			120	120	200
21 	13568436656	192.168.100.18	www.alibaba.com	2481	24681	200
22 	13568436656	192.168.100.19			1116	954	200

 */

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;

/**
 * 统计每一个手机号耗费的总上行流量、下行流量、总流量
 */
public class SerializeMr {

    static class FlowCountMapper extends Mapper<LongWritable, Text, Text, FlowBean> {
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            String line = value.toString();
            String[] fields = line.split("\t");

            //手机号码
            Text k = new Text(fields[1]);

            FlowBean v = new FlowBean(Long.parseLong(fields[fields.length - 3]),
                    Long.parseLong(fields[fields.length - 2]),
                    0L);

            context.write(k, v);

        }
    }

    static class FlowCountReducer extends Reducer<Text, FlowBean, Text, FlowBean> {
        @Override
        protected void reduce(Text key, Iterable<FlowBean> values, Context context) throws IOException, InterruptedException {

            long sum_upFlow = 0;
            long sum_downFlow = 0;

            for (FlowBean flowBean : values) {
                sum_upFlow += flowBean.getUpFlow();
                sum_downFlow += flowBean.getDownFlow();
            }

            context.write(key, new FlowBean(sum_upFlow, sum_downFlow, sum_downFlow + sum_upFlow));

        }
    }


    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        // 输入输出路径需要根据自己电脑上实际的输入输出路径设置
        args = new String[]{"mr/phone_data.txt", "mr/out/flowMR"};

        // 1 获取配置信息，或者job对象实例
        Configuration conf = new Configuration();
        conf.set("mapreduce.framework.name", "local");
        Job job = Job.getInstance(conf);

        // 6 指定本程序的jar包所在的本地路径
        job.setJarByClass(SerializeMr.class);

        // 2 指定本业务job要使用的mapper/Reducer业务类
        job.setMapperClass(FlowCountMapper.class);
        job.setReducerClass(FlowCountReducer.class);

        // 3 指定mapper输出数据的kv类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(FlowBean.class);

        // 4 指定最终输出的数据的kv类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(FlowBean.class);

        // 5 指定job的输入原始文件所在目录
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));

        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }

}
